Monetizing User Activity on Social Networks

Meena Nagarajan, Amit P. Sheth KNO.E.SIS Center Wright State University M. Nagarajan, K. Baid, A. P. Sheth, and S. Wang, "Monetizing User Activity on Social Networks -‐ Challenges and Experiences“, 2009 IEEE/WIC/ACM International Conference on Web Intelligence, Milan, Italy

  On social networks   Use case for this talk   Targeted content = content-‐based advertisements   Target = user proﬁles   Content-‐based advertisements CBAs   Well-‐known monetization model for online content

May 30,June 02 2009

June 01, 2009

  Interests do not translate to purchase intents   Interests are often outdated..   Intents are rarely stated on a proﬁle..   Cases that work   New store openings, sales   Highly demographic-‐targeted ads

June 01, 2009

CONTENT-‐BASED ADS ON THEIR PROFILES June 01, 2009

  Non-‐trivial   Non-‐policed content ▪  Brand image, Unfavorable sentiments1   People are there to network ▪  User attention to ads is not guaranteed   Informal, casual nature of content ▪  People are sharing experiences and events ▪  Main message overloaded with oﬀ topic content I NEED HELP WITH SONY VEGAS PRO 8!! Ugh and i have a video project due tomorrow for merrill lynch :(( all i need to do is simple: Extract several scenes from a clip, insert captions, transitions and thats it. really. omgg i cant ﬁgure out anything!! help!! and i got food poisoning from eggs. its not fun. Pleasssse, help? :( 1Learning from Multi-‐topic Web Documents for Contextual Advertisement, Zhang, Y., Surendran, A. C., Platt, J. C., and Narasimhan, M. , KDD 2008

  Cultural Entities HOW   Word Usages in self-‐ presentation WHY   Slang sentiments WHAT   Intentions

  Identifying intents behind user posts on social networks   Content with monetization potential   Identifying keywords for advertizing in user-‐ generated content   Interpersonal communication & oﬀ-‐topic chatter

  User studies   Hard to compare activity based ads to s.o.t.a   Impressions to Clickthroughs   How well are we able to identify monetizable posts   How targeted are ads generated using our keywords vs. entire user generated content

Identiﬁcation, Evaluation

  Scribe Intent not same as Web Search Intent1   People write sentences, not keywords or phrases   Presence of a keyword does not imply navigational / transactional intents   ‘am thinking of getting X’ (transactional)   ‘i like my new X’ (information sharing)   ‘what do you think about X’ (information seeking) 1B. J. Jansen, D. L. Booth, and A. Spink, “Determining the informational, navigational, and transactional intent of web queries,” Inf. Process. Manage., vol. 44, no. 3, 2008.

  Action patterns surrounding an entity   How questions are asked and not topic words that indicate what the question is about   “where can I ﬁnd a chotto psp cam”   User post also has an entity

Set of user posts from SNSs Not annotated for presence or absence of any intent

Generate a universal set of n-‐gram patterns; freq > f S = set of all 4-‐grams; freq > 3

Generate set of candidate patterns from seed words (why,when,where,how,what) Sc = all 4-‐grams in S that extract seed words

User picks 10 seed patterns from Sc Sis = ‘does anyone know how’, ‘where do i ﬁnd’, ‘someone tell me where’….

Sc = all 4-‐grams in S that Sis = ‘does anyone know how’, ‘where extract seed words do i ﬁnd’, ‘someone tell me where’…. Gradually expand Sis by adding Information Seeking patterns from Sc

Sis = ‘does anyone know how’, ‘where do i ﬁnd’, ‘someone tell me where’…. For every pis in Sis generate set of ﬁller patterns

‘.* anyone know how’ ‘does anyone .* how’ ‘does .* know how’ ‘does anyone know .*’ ‘does anyone know how’ Look for patterns in Sc -‐ Functional compatibility of ﬁller -‐ words used in similar semantic contexts -‐ Empirical support for ﬁller

  Functional properties / communicative functions of words   From a subset of LIWC1   cognitive mechanical (e.g., if, whether, wondering, ﬁnd) ▪  ‘I am thinking about getting X’   adverbs (e.g., how, somehow, where)   impersonal pronouns (e.g., someone, anybody, whichever) ▪  ‘Someone tell me where can I ﬁnd X’ 1Linguistic Inquiry Word Count,LIWC, http://liwc.net

  Sc = {‘does anyone know how’, ‘where do I ﬁnd’, ‘someone tell me where’}   pis = `does anyone know how’   ‘does * know how’   ‘does someone know how’ ▪  Functional Compatibility -‐ Impersonal pronouns ▪  Empirical Support – 1/3   ‘does somebody know how’ ▪  Functional Compatibility -‐ Impersonal pronouns ▪  Empirical Support – 0 ▪  Pattern Retained   ‘does john know how’ ▪  Pattern discarded

  Over iterations, single-‐word substitutions, functional usage and empirical support conservatively expands Sis   Infusing new patterns and seed words   Stopping conditions

  does anyone know how   no idea how to   anyone know how to   someone tell me how   i dont know what   have no clue what   know where i can   does anyone know if   tell me how to   i dont know if   i dont know how   know if i can   anyone know where i   anyone know if i   does anyone know where   im not sure if   does anyone know what   i was wondering if   anybody know how to   idea what you are   anyone know how i   let me know how   im not sure what   and i dont know   does anybody know how   now i dont know   does anyone know why   but i dont really   i was wondering how   was wondering if someone   does anyone know when   would like to see   tell me what to   see what i can   im not sure how   anyone have any idea   i was wondering what   wondering if someone could   was wondering how i   i do not want

  Information Seeking patterns generated oﬄine   Information seeking intent score of a post   Extract and compare patterns in posts with extracted patterns   Transactional intent score of a post ▪  LIWC ‘Money’ dictionary ▪  173 words and word forms indicative of transactions, e.g., trade, deal, buy, sell, worth, price etc.

  Training corpus   8000 user posts ▪  MySpace Computers, Electronics, Gadgets forum   309 unique new patterns, 263 unambiguous   Testing patterns for recall   ‘To buy’ Marketplace – average 81 %

Oﬀ-‐topic noise elimination

  Identifying keywords in monetizable posts   Plethora of work in this space   Oﬀ-‐topic noise removal is our focus I NEED HELP WITH SONY VEGAS PRO 8!! Ugh and i have a video project due tomorrow for merrill lynch :(( all i need to do is simple: Extract several scenes from a clip, insert captions, transitions and thats it. really. omgg i cant ﬁgure out anything!! help!! and i got food poisoning from eggs. its not fun. Pleasssse, help? :(

  Topical hints   C1 -‐ ['camcorder']   Keywords in post   C2 -‐ ['electronics forum', 'hd', 'camcorder', 'somethin', 'ive', 'canon', 'little camera', 'canon hv20', 'cameras', 'oﬀtopic']   Move strongly related keywords from C2 to C1 one-‐by-‐one   Relatedness determined using information gain   Using the Web as a corpus, domain independent

  C1 -‐ ['camcorder']   C2 -‐ ['electronics forum', 'hd', 'camcorder', 'somethin', 'ive', 'canon', 'little camera', 'canon hv20', 'cameras', 'oﬀtopic']   Informative words   ['camcorder', 'canon hv20', 'little camera', 'hd', 'cameras', 'canon']

Preliminary work

  Keywords from 60 monetizable user posts   Monetizable intent, at least 3 keywords in content   45 MySpace Forums, 15 Facebook Marketplace, 30 graduate students   10 sets of 6 posts each   Each set evaluated by 3 randomly selected users   Monetizable intents?   All 60 posts voted as unambiguously information seeking in intent

  Google AdSense ads for user post vs. extracted topical keywords

  Choose relevant Ad Impressions   VW 6 disc CD changer   I need one thats compatible with a 2000 golf most are sold from years 1998-‐2004if anyone has one [or can get one] PLEASE let me know!

  Users picked ads relevant to the post   At least 50% inter-‐evaluator agreement   For the 60 posts   Total of 144 ad impressions   17% of ads picked as relevant   For the topical keywords   Total of 162 ad impressions   40% of ads picked as relevant

  User’s proﬁle information   Interests, hobbies, tv shows..   Non-‐demographic information   Submit a post   Looking to buy and why (induced noise)   Ads that generate interest, captured attention

  Using proﬁle ads   Total of 56 ad impressions   7% of ads generated interest   Using authored posts   Total of 56 ad impressions   43% of ads generated interest   Using topical keywords from authored posts   Total of 59 ad impressions   59% of ads generated interest

  User studies small and preliminary, clearly suggest   Monetization potential in user activity   Improvement for Ad programs in terms of relevant impressions   Evaluations based on forum, marketplace   Verbose content   Status updates, notes, community and event memberships…   One size may not ﬁt all

  A world between relevant impressions and clickthroughs   Objectionable content, vocabulary impedance, Ad placement, network behavior   In a pipeline of other community eﬀorts   No proﬁle information taken into account   Cannot custom send information to Google AdSense

  Keywords to Ad Impressions   Google Adsense like web service   Monetization potential of a keyword on the Web not the same on a social n/w?   Ranking keywords in user post   We are building an F8 app   Collaboration for clickthrough data

  Google/Bing: Meena Nagarajan   meena@knoesis.org   http://knoesis.wright.edu/students/meena/   Google/Bing: Amit Sheth   amit@knoesis.org   http://knoesis.wright.edu/amit

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Monetizing User Activity on Social Networks

by Meena Nagarajan on Nov 05, 2009

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Monetizing User Activity on Social Networks — Presentation Transcript